Bibliographic record
Abstract
1-m class telescopes are arguably the workhorses of modern astronomy and represent an excellent return of science for a relatively modest capital investment.Such instruments can be used in large-field survey work, high precision photometry as well as in providing HQP opportunities for undergraduate and graduate students.Currently there exists a dearth of such instruments in Canada and in the prairie provinces specifically.In this poster we argue for the development of a 1-m, robotic instrument to be situated in western Canada.The proposed instrument will address two central concerns.First, the instrument that we envision will be multi-purpose and through appropriate optical design will function as both a wide field survey instrument and a narrow field instrument capable of high precision photometry.A remote, robotic access telescope will also maximize on-sky efficiency and data output.Second, this telescope will serve as a prototype for a similar remote telescope for the high arctic.Lessons learned in this project should provide valuable insights into many of the issues expected for operation of a remote telescope in the arctic (extreme cold, problems of data transmission etc).We solicit comments and expressions of interest from other researchers who would benefit from such an instrument. GOALS
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".